Visibility: public Uploaded 13-08-2021 by Sergey Redyuk sklearn==0.18 numpy>=1.6.1 scipy>=0.9 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.18
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Multi-layer Perceptron classifier. This model optimizes the log-loss function using LBFGS or stochastic gradient descent. .. versionadded:: 0.18


activationdefault: "relu"
alphaL2 penalty (regularization term) parameterdefault: 0.0001
batch_sizeSize of minibatches for stochastic optimizers If the solver is 'lbfgs', the classifier will not use minibatch When set to "auto", `batch_size=min(200, n_samples)` learning_rate : {'constant', 'invscaling', 'adaptive'}, default 'constant' Learning rate schedule for weight updates - 'constant' is a constant learning rate given by 'learning_rate_init' - 'invscaling' gradually decreases the learning rate ``learning_rate_`` at each time step 't' using an inverse scaling exponent of 'power_t' effective_learning_rate = learning_rate_init / pow(t, power_t) - 'adaptive' keeps the learning rate constant to 'learning_rate_init' as long as training loss keeps decreasing Each time two consecutive epochs fail to decrease training loss by at least tol, or fail to increase validation score by at least tol if 'early_stopping' is on, the current learning rate is divided by 5 Only used when ``solver='sgd'``default: "auto"
beta_1Exponential decay rate for estimates of first moment vector in adam, should be in [0, 1). Only used when solver='adam'default: 0.9
beta_2Exponential decay rate for estimates of second moment vector in adam, should be in [0, 1). Only used when solver='adam'default: 0.999
early_stoppingWhether to use early stopping to terminate training when validation score is not improving. If set to true, it will automatically set aside 10% of training data as validation and terminate training when validation score is not improving by at least tol for two consecutive epochs Only effective when solver='sgd' or 'adam'default: false
epsilonValue for numerical stability in adam. Only used when solver='adam'default: 1e-08
hidden_layer_sizesThe ith element represents the number of neurons in the ith hidden layer activation : {'identity', 'logistic', 'tanh', 'relu'}, default 'relu' Activation function for the hidden layer - 'identity', no-op activation, useful to implement linear bottleneck, returns f(x) = x - 'logistic', the logistic sigmoid function, returns f(x) = 1 / (1 + exp(-x)) - 'tanh', the hyperbolic tan function, returns f(x) = tanh(x) - 'relu', the rectified linear unit function, returns f(x) = max(0, x) solver : {'lbfgs', 'sgd', 'adam'}, default 'adam' The solver for weight optimization - 'lbfgs' is an optimizer in the family of quasi-Newton methods - 'sgd' refers to stochastic gradient descent - 'adam' refers to a stochastic gradient-based optimizer proposed by Kingma, Diederik, and Jimmy Ba Note: The default solver 'adam' works pretty well on relatively large datasets (with thousands of training samples or more) in terms of both training t...default: [100]
learning_ratedefault: "constant"
learning_rate_initThe initial learning rate used. It controls the step-size in updating the weights. Only used when solver='sgd' or 'adam'default: 0.001
max_iterMaximum number of iterations. The solver iterates until convergence (determined by 'tol') or this number of iterationsdefault: 200
momentumMomentum for gradient descent update. Should be between 0 and 1. Only used when solver='sgd'default: 0.9
nesterovs_momentumWhether to use Nesterov's momentum. Only used when solver='sgd' and momentum > 0default: true
power_tThe exponent for inverse scaling learning rate It is used in updating effective learning rate when the learning_rate is set to 'invscaling'. Only used when solver='sgd'default: 0.5
random_stateState or seed for random number generatordefault: null
shuffleWhether to shuffle samples in each iteration. Only used when solver='sgd' or 'adam'default: true
solverdefault: "adam"
tolTolerance for the optimization. When the loss or score is not improving by at least tol for two consecutive iterations, unless `learning_rate` is set to 'adaptive', convergence is considered to be reached and training stopsdefault: 0.0001
validation_fractionThe proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1 Only used if early_stopping is Truedefault: 0.1
verboseWhether to print progress messages to stdoutdefault: false
warm_startWhen set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solutiondefault: false


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